Adds instructions for using pre-made locomanipulation SDG dataset/model (#5357)

# Description

Adds two tip callouts to the Demo 3 (G1 locomanipulation) section of the
humanoids imitation learning documentation, giving users shortcuts to
skip
expensive pipeline steps:
   
1. Pre-made dataset (nvidia/g1_locomanip_dataset on Hugging Face):
placed at the start of the SDG generation section, allowing users to
skip manipulation
dataset generation, SDG generation, and LeRobot conversion, and proceed
directly to finetuning. Includes huggingface-cli download + unzip
commands with the
exact extracted path (g1_simple_high_var_lerobot/) and a note that
policies trained on this dataset require --policy_quat_format wxyz at
rollout.
2. Pre-trained model (nvidia/g1_locomanip_finetune on Hugging Face):
placed immediately before the rollout section, allowing users to skip
finetuning
entirely. Includes download + unzip commands with the exact checkpoint
path (g1_locomanip_finetune_20260129_231610/checkpoint-20000) and the
required
  --policy_quat_format wxyz flag.

Fixes # (issue)


## Type of change

- Documentation update

## Screenshots

## Checklist

- [x] I have read and understood the [contribution
guidelines](https://isaac-sim.github.io/IsaacLab/main/source/refs/contributing.html)
- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [ ] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

Author: jaybdub

docs/source/overview/imitation-learning/humanoids_imitation.rst

@@ -538,6 +538,24 @@ Generate the dataset with manipulation and point-to-point navigation
 
 To create a comprehensive locomanipulation dataset that combines both manipulation and navigation capabilities, you can generate a navigation dataset using the manipulation dataset from the previous step as input.
 
+.. tip::
+
+   **Skip data generation:** A pre-made locomanipulation dataset in LeRobot format is available on
+   Hugging Face at `nvidia/g1_locomanip_dataset <https://huggingface.co/datasets/nvidia/g1_locomanip_dataset>`__.
+   Downloading it lets you skip this section and the dataset conversion step, proceeding directly to
+   **Finetune the policy** below.
+
+   Download and unzip the dataset:
+
+   .. code:: bash
+
+      huggingface-cli download nvidia/g1_locomanip_dataset --repo-type dataset --local-dir ./datasets/g1_locomanip_hf
+      unzip ./datasets/g1_locomanip_hf/*.zip -d ./datasets/
+
+   The archive extracts to ``./datasets/g1_simple_high_var_lerobot/``.
+   Use this path as the ``--dataset-path`` in the finetuning step.
+   Policies trained on this dataset require ``--policy_quat_format wxyz`` at rollout time.
+
 .. list-table::
    :widths: 50 50
    :header-rows: 0
@@ -690,6 +708,23 @@ Run finetuning from the **Isaac-GR00T** repository root. Use the LeRobot-format
 
 See the GR00T N1.5 repository documentation for additional training options.
 
+.. tip::
+
+   **Skip finetuning:** A pre-trained GR00T N1.5 checkpoint for this task is available on
+   Hugging Face at `nvidia/g1_locomanip_finetune <https://huggingface.co/nvidia/g1_locomanip_finetune>`__.
+   Downloading it lets you skip the finetuning step and proceed directly to rollout.
+
+   Download and unzip the checkpoint:
+
+   .. code:: bash
+
+      huggingface-cli download nvidia/g1_locomanip_finetune --local-dir ./checkpoints/g1_locomanip_finetune_hf
+      unzip ./checkpoints/g1_locomanip_finetune_hf/*.zip -d ./checkpoints/
+
+   The archive extracts to ``./checkpoints/g1_locomanip_finetune_20260129_231610/``.
+   Use ``./checkpoints/g1_locomanip_finetune_20260129_231610/checkpoint-20000`` as the ``--model_path``
+   in the rollout command below. This checkpoint requires ``--policy_quat_format wxyz``.
+
 Rollout the policy in Isaac Lab
 """""""""""""""""""""""""""""""